Identification of oxalotrophic bacteria by neural network analysis of numerical phenetic data

A new approach with artificial neural network (ANN) was applied to numerical taxonomy of bacteria using the oxalate as carbon and energy source. For this aim the characters effective in differentiating separate groups were selected from morphological, physiological and biochemical test results. Fourteen aerobic, Gram-negative, oxalate-utilizing isolates and four oxalate-utilizing reference strains (Ralstonia eutropha DSM 428, Methylobacterium extorquens DSM 1337(T), Ralstonia oxalatica DSM 1105(T), Oxalicibacterium flavum DSM 15506(T)) were included in the study. ANN program used here was developed in Borland C++ language. Iterations were performed on an IBM compatible PC computer. ANN architecture having feed-forward backpropagation algorithm was used for teaching generalized 6 rule. The results show that ANN can have a large potential in solving the taxonomic problems of oxalate-utilizing bacteria.